INDUSTRIAL ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
BME3005	Biostatistics	Fall Spring	2	2	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi BURCU TUNÇ ÇAMLIBEL
Course Lecturer(s):	Dr. Öğr. Üyesi BURCU TUNÇ ÇAMLIBEL
Recommended Optional Program Components:	None
Course Objectives:	- The course provides an introduction to selected important topics in biostatistical concepts and reasoning. This course represents an introduction to the field and provides a survey of data and data types. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; issues of power and sample size in study designs; and random sample and other study types; regression analysis, confidence intervals, correlations

Learning Outcomes

The students who have succeeded in this course;
- The students who have succeeded in this course;
I. Interpret statistical results correctly, effectively, and in context.
II. Select an appropriate test for comparing two or more populations, and interpret and explain a p-value
III. Understand the concept of the power of data.
IV. Calculate and interpret confidence intervals for population means and proportions
V. Understand regression analysis and correlation of variables.

Course Content

Design of Experiments, Statistical programming: , Exploratory Data Analysis and Descriptive Statistics, Probability Theory, Sampling Distributions and the Central Limit Theorem, Estimation, Statistical Inference, Contingency tables, Nonparametric Tests, Power and sample size, ANOVA, Correlation and Regression, Logistic regression, Survival Analysis, applications on biological datasets.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to biostatistics
2)	Descriptive Statistics
3)	Probability Theory
4)	Sampling Distributions and the Central Limit Theorem
5)	ANOVA
6)	The Special Case of Two Groups: the t test
7)	Contingency tables, Chi Square Test, z-test
8)	Fisher Exact Test, Relative Risk, Odds Ratio
9)	Power and Sample size
10)	Paired t-test, Repeated Measures of Analysis of Variance, McNemar's Test
11)	Nonparametric Tests: Mann-Whitney Rank-Sum Test, Wilcoxon Signed-Rank Test
12)	Nonparametric Tests: Kruskal-Wallis Test, Friedman Test
13)	Confidence Intervals
14)	Correlation and Regression

Sources

Course Notes / Textbooks:	Primer of Biostatistics, Stanton A. Glantz, McGraw-Hill, 7th Edition Fundamental of Biostatistics, Bernard Rosner, Cengage Learning, 8th Edition
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	5	% 30
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	7	98
Quizzes	5	1	5
Midterms	1	3	3
Final	1	3	3
Total Workload			151

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Build up a body of knowledge in mathematics, science and industrial engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems.
2)	Identify, formulate, and solve complex engineering problems; select and apply proper analysis and modeling methods for this purpose.
3)	Design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. The ability to apply modern design methods to meet this objective.
4)	Devise, select, and use modern techniques and tools needed for solving complex problems in industrial engineering practice; employ information technologies effectively.
5)	Design and conduct experiments, collect data, analyze and interpret results for investigating the complex problems specific to industrial engineering.
6)	Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working independently.
7)	Demonstrate effective communication skills in both oral and written English and Turkish. Writing and understanding reports, preparing design and production reports, making effective presentations, giving and receiving clear and understandable instructions.
8)	Recognize the need for lifelong learning; show ability to access information, to follow developments in science and technology, and to continuously educate him/herself.
9)	Develop an awareness of professional and ethical responsibility, and behaving accordingly. Information about the standards used in engineering applications.
10)	Know business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
11)	Know contemporary issues and the global and societal effects of modern age engineering practices on health, environment, and safety; recognize the legal consequences of engineering solutions.
12)	Develop effective and efficient managerial skills.